
The dataset includes only the original tweets as retweets were discarded in the early stage of processing the tweets. This is based on the assumption that retweets may not have sufficient information to contribute to the classification task.

Despite having a strict and robust annotation process, the annotators faced several challenges during annotations, especially for annotating unrealistic hope. Sometimes, the tweet may represent hope but wishing for something harmful (e.g., I hope all of them will be killed #USER# #USER# #USER#); in such cases, some annotators believe that these tweets support the definition of having a desire to something happen, and also they were future-oriented; even though in the form of anger and sadness. On the other hand, some annotators believed that hope is always associated with expecting a favorable future (where the first group thought the hoper might think of death as a positive outcome for himself). Therefore, we gave freedom to label the tweets based on the annotators' understanding of tweets. However, in most cases, these types of tweets were labeled as unrealistic hope since they are irrational, with no or less likelihood of occurrence. In several cases, annotators believed some tweets represented regret rather than a wish or hope and labeled them as not hope since they are not future-oriented (e.g., #USER# my paternal grandparent died long ago, I wish I had spent more time with them).

The proposed dataset tackles the concept of hope based on the definition given by Psychologists (see  INTRODUCTION) rather than only taking positivity and optimism as hope. Considering Figure REF  and REF  the definition of hope as a future-oriented expectation, desire, wish and want is justified. The Spacy NER tagger was used for named-entity extraction in the dataset, and Figure REF  represents the NER tags over the dataset. It is observed that DATE (this tag indicates date and time) is the most frequent tag, and the samples show that they are often future-oriented.

The readability of tweets was evaluated using textstathttps://pypi.org/project/textstat/ library. It is an easy tool to determine readability, complexity, and grade level. The result of the readability analysis shown in Figure REF  illustrates that most tweets scored a readability score of more than 50, which means that the dataset is relatively readable.

The low IAA score of HopeEDI [1]} (IAA score of 63% for binary hope speech detection in English YouTube comments) proved the difficulty of the annotation process for hope speech detection. Therefore, our strict annotator selection and strong guidelines resulted in an IAA of 0.85% for binary and 82% in multiclass classifications in the proposed dataset.

The performance of the models for both subtasks is entirely in line with IAA scores which reveals the validity of our dataset. Further, it shows that annotators and learning models struggled to classify 20% (\(\pm \)  5%) of tweets in the dataset.

<FIGURE><FIGURE><FIGURE><FIGURE>